Senior Data Engineer
Cleveland, Ohio
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- Pay not listed
- Work setting
- Not stated
- Employment
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What you’ll work on
Full postingBuild scalable ELT/ETL pipelines from legacy databases, ERPs and APIs into cloud warehouses
Refactor inefficient queries and data models to cut latency and cost
Build automated data quality tests that catch anomalies before production
Build data pipelines for AI workloads, including vector databases for RAG
Mentor engineers through code reviews and improved coding standards
From the employer’s posting
Architect and build scalable, high-volume ELT/ETL pipelines that ingest complex data sets from diverse client systems, including legacy on-premise databases, ERPs, and third-party APIs, into centralized cloud warehouses.
refactoring inefficient queries and data models to significantly reduce latency and operational costs for enterprise-scale environments
Design and implement comprehensive data quality frameworks, ensuring automated testing and validation logic catches anomalies before they reach production models or executive dashboards.
Design and implement data pipelines specifically for AI workloads, including the management of vector databases to support Retrieval-Augmented Generation (RAG) and model inference.
Provide technical mentorship to the engineering team, conducting rigorous code reviews and driving improvements in coding standards, documentation, and system reliability.
What you’ll bring
All qualificationsPreferred experience
- Experience architecting feature stores or data pipelines specifically for ML workloads.
Qualification wording
Experience architecting feature stores or data pipelines specifically for ML workloads.
Tools in this posting
- Python
- SQL
- AWS
- Azure
- BigQuery
- dbt
- Docker
- GCP
- PostgreSQL
- Snowflake
- Terraform
- Airflow
Source — Tool mentions in context
- 6+ years of data engineering experience with a focus on modern cloud platforms. - Expert-level proficiency in Python and SQL is mandatory. - Strong software engineering background with proficiency in Python for building custom integrations and data tools.
- Expert-level proficiency in Python and SQL is mandatory. - Strong software engineering background with proficiency in Python for building custom integrations and data tools. - Deep production experience with dbt for data modeling and Airflow for orchestration.
- Deep production experience with dbt for data modeling and Airflow for orchestration. - Advanced knowledge of cloud-native data suites within GCP (preferred), AWS, or Azure. - Proficiency in managing environment reproducibility using Terraform and Docker, ensuring pipelines are CI/CD compliant.
- Proficiency in managing environment reproducibility using Terraform and Docker, ensuring pipelines are CI/CD compliant. - Expert knowledge of cloud data warehouses (BigQuery or Snowflake) and their internal architecture. - Experience with AI workflows and data systems like vector databases, PostgreSQL, Vertex AI, Pub/Sub.
- Strong software engineering background with proficiency in Python for building custom integrations and data tools. - Deep production experience with dbt for data modeling and Airflow for orchestration. - Advanced knowledge of cloud-native data suites within GCP (preferred), AWS, or Azure.
- Provide technical mentorship to the engineering team, conducting rigorous code reviews and driving improvements in coding standards, documentation, and system reliability. - Standardize the organization’s approach to the Modern Data Stack (MDS), driving the adoption of best practices in dbt, Airflow (Cloud Composer), and Beam (Dataflow) across diverse client engagements. Our total rewards program is designed for your protection, peace of mind, and overall well-being. In addition to our outstanding basics, we offer a net-zero cost medical option, company contributions to your HSA, fertility support, fully-paid parental leave, a monthly stipend for your lifestyle spending account, and much more.
- Advanced knowledge of cloud-native data suites within GCP (preferred), AWS, or Azure. - Proficiency in managing environment reproducibility using Terraform and Docker, ensuring pipelines are CI/CD compliant. - Expert knowledge of cloud data warehouses (BigQuery or Snowflake) and their internal architecture.
- Expert knowledge of cloud data warehouses (BigQuery or Snowflake) and their internal architecture. - Experience with AI workflows and data systems like vector databases, PostgreSQL, Vertex AI, Pub/Sub. Preferred Qualifications
Posting record
Report an errorMay be filledThe employer has taken this listing down, so the role may be filled. Check their website before applying.
- Status in our records
- No longer listed
- First seen by us
- Jun 2, 2026 · 144 days ago
- Recorded sightings
- 33
- Last seen by us
- Oct 7, 2026
These dates show when we found the listing. Check the employer’s website to confirm it is still accepting applications.
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Job description
WE’RE HIRING! If you love data and are looking for unlimited growth opportunities, we want to talk with you about joining Further.
Further is a data, cloud, and AI company whose focus is helping companies turn raw data into the right decisions. We have an award winning culture of extraordinary people. Our purpose is to enable people to thrive so that businesses can thrive. We believe that the work you do should matter - it should be meaningful to you professionally and personally, and it should have a positive impact on both you and our clients. If this sounds exciting to you, let’s chat!
SENIOR DATA ENGINEER
We are looking for a highly skilled and strategic Senior Data Engineer to lead in our data engineering consulting team. In this role, you will serve as the technical cornerstone for our clients, designing and deploying the sophisticated data architectures required to support production-grade Artificial Intelligence and Machine Learning applications. This is a high-impact role where you will set the standards for data quality, modeling, and orchestration across the organization. You will navigate diverse technical environments, and your work will directly enable the transition from experimental AI prototypes to resilient, enterprise-scale systems that deliver measurable business value.
What experience should you have:
- 6+ years of data engineering experience with a focus on modern cloud platforms.
- Expert-level proficiency in Python and SQL is mandatory.
- Strong software engineering background with proficiency in Python for building custom integrations and data tools.
- Deep production experience with dbt for data modeling and Airflow for orchestration.
- Advanced knowledge of cloud-native data suites within GCP (preferred), AWS, or Azure.
- Proficiency in managing environment reproducibility using Terraform and Docker, ensuring pipelines are CI/CD compliant.
- Expert knowledge of cloud data warehouses (BigQuery or Snowflake) and their internal architecture.
- Experience with AI workflows and data systems like vector databases, PostgreSQL, Vertex AI, Pub/Sub.
Preferred Qualifications
- Experience architecting feature stores or data pipelines specifically for ML workloads.
- The ability to navigate diverse technical environments and corporate cultures while maintaining a high standard of delivery and client satisfaction.
- A commitment to software engineering best practices, including version control, unit testing, and comprehensive documentation.
- An analytical thinker who anticipates scaling bottlenecks and security vulnerabilities before they impact production.
What you’ll be doing in this role:
- Architect and build scalable, high-volume ELT/ETL pipelines that ingest complex data sets from diverse client systems, including legacy on-premise databases, ERPs, and third-party APIs, into centralized cloud warehouses.
- Lead the audit and optimization of existing data workflows, refactoring inefficient queries and data models to significantly reduce latency and operational costs for enterprise-scale environments.
- Design and implement comprehensive data quality frameworks, ensuring automated testing and validation logic catches anomalies before they reach production models or executive dashboards.
- Design and implement data pipelines specifically for AI workloads, including the management of vector databases to support Retrieval-Augmented Generation (RAG) and model inference.
What you'll need to accomplish in your first year:
- Own the roadmap for data platform stability and scalability enhancements, ensuring the infrastructure can support the evolving needs of both corporate IT and AI research teams.
- Provide technical mentorship to the engineering team, conducting rigorous code reviews and driving improvements in coding standards, documentation, and system reliability.
- Standardize the organization’s approach to the Modern Data Stack (MDS), driving the adoption of best practices in dbt, Airflow (Cloud Composer), and Beam (Dataflow) across diverse client engagements.
Our total rewards program is designed for your protection, peace of mind, and overall well-being. In addition to our outstanding basics, we offer a net-zero cost medical option, company contributions to your HSA, fertility support, fully-paid parental leave, a monthly stipend for your lifestyle spending account, and much more.
Apply today or check out all our opportunities!
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Source details
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- Pay
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- Location & working pattern
Cleveland, Ohio
Apply today or check out all our opportunities! #LI-Hybrid By submitting your application, you consent to our collection, processing and disclosure of the contained personal data in accordance with our data practices. If you are a resident of the U.S. state of California, you can read about our data practices and your related privacy rights here. If you are a resident of the European Economic Area, Switzerland or the United Kingdom, you can read about our data practices and your related privacy rights here.
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